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Multiculturalism and crisis of identity: the experience of the USAandCanada

2023· article· en· W4390777818 on OpenAlexaboutno aff
K.A. Sakhiyeva, Saniya Nurdavletova, L.K. Akhmetzhanova, Aliya Akatayeva

Bibliographic record

VenueBULLETIN of the L N GUMILYOV EURASIAN NATIONAL UNIVERSITY POLITICAL SCIENCE REGIONAL STUDIES ORIENTAL STUDIES TURKOLOGY Series · 2023
Typearticle
Languageen
FieldSocial Sciences
TopicCentral Asia Education and Culture
Canadian institutionsnot available
Fundersnot available
KeywordsMulticulturalismEthnic groupIdentity (music)State (computer science)National identityPolitical sciencePopulationIdentity crisisCultural identityPolitical economyGender studiesSociologySocial scienceLawAestheticsNegotiationArtDemographyFace (sociological concept)

Abstract

fetched live from OpenAlex

Thearticleprovidesanoverviewofinternationalexperiences,namelythepracticesof the United States of America and Canada in which «multiculturalism» acts as a state policyin defining national identity in the area of cultural policy, in the contemporary world theproblemofovercomingthecrisisofthenationalidentityforanystateisoneofthemostessentialtopics.ThepaperalsoshowsthatCanadaandtheUnitedStatesofAmericaaresomeofthefewcountriesintheworldthatusethemodelofmulticulturalismasanintegrationpolicy,have a multi-ethnic population, and receive hundreds of thousands of temporary or permanentimmigrants every year. The policy of multiculturalism, as oneof the models of integrationpolicy,isusedinthesestatesasoneofthewaystoachievethisgoal.Thepurposeoftheauthorsistoanalyzethepoliciesandactionsoftheseforeigncountriesandtoshowthatthecallingofamulticulturalpolicyisconsideredtobetheconsolidationofa fragmented society by ethno-cultural characteristics, through the comprehensive support ofculturaldiversity.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.003
metaresearch head score (Gemma)0.004
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.590
Threshold uncertainty score0.815

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.004
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0020.004
Science and technology studies0.0580.016
Scholarly communication0.0160.007
Open science0.0030.018
Research integrity0.0050.010
Insufficient payload (model declined to judge)0.0100.001

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.048
GPT teacher head0.336
Teacher spread0.287 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations0
Published2023
Admission routes1
Has abstractyes

Explore more

Same venueBULLETIN of the L N GUMILYOV EURASIAN NATIONAL UNIVERSITY POLITICAL SCIENCE REGIONAL STUDIES ORIENTAL STUDIES TURKOLOGY SeriesSame topicCentral Asia Education and CultureFrench-language works237,207